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Data: guides & tutorials
Web data basics: crawl, parse, and validate datasets for engineers and analysts. Scale ethical collection with Apify actors, proxies, and schedules.
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Working with web data means crawling, parsing, and validating it into datasets analysts and engineers can trust. These guides cover the fundamentals of collecting clean data ethically and at scale.
Reliable data work pairs good extraction with validation and refresh schedules so datasets stay current. Apify actors, proxies, and schedules handle the collection layer. Below you will find practical guides for building dependable web datasets.
Real estate investors and analysts need data: market comps, rental yields, price trends, days on market, and new listing alerts. Public listing sites (Zillow, Realtor.com, Redfin, Rightmove, Idealista) hold this data, but their official APIs are limited or expensive. Web scraping fills the gap. Apify offers Zillow and Realtor.com scrapers in the Store. This guide covers use cases, data schema, workflows, legal considerations, and MLS alternatives.
Make.com Data Stores are built-in key-value databases that let your scenarios remember data between runs — without connecting to an external database.
Every Make scenario is stateless by default. Run a scenario twice and it has no memory of the first execution. Data Stores break that constraint. They give you a lightweight, persistent layer that lives inside Make, costs no extra infrastructure, and integrates directly with your modules through a dedicated set of operations.
This guide covers everything: creating a Data Store, defining its structure, performing all CRUD operations, running searches, and using Data Stores to solve three real-world problems — deduplication, API response caching, and scenario state management.
